Optimization of Apparel Supply Chain Using Deep Reinforcement Learning

Abstract

An effective supply chain management system is indispensable for an enterprise with a supply chain network in several aspects. Especially, organized control over the production and transportation of its products is a key success factor for the enterprise to stay active without damaging its reputation. This case is also highly relevant to garment industries. In this study, an extensive Deep Reinforcement Learning study for apparel supply chain optimization is proposed and undertaken, with focus given to Soft Actor-Critic. Six models are experimented with in this study and are compared with respect to the sell-through rate, service level, and inventory-to-sales ratio. Soft Actor-Critic outperformed several other state-of-the-art Actor Critic models in managing inventories and fulfilling demands. Furthermore, explicit indicators are calculated to assess the performance of the models in the experiment. Soft Actor-Critic achieved a better balance between service level and sell-through rate by ensuring higher availability of the stocks to sell without overstocking. From numerical experiments, it has been shown that S-policy, Trust Region Policy Optimization, and Twin Delayed Deep Deterministic Policy Gradient have a good balance between service level and sell-through rate. Additionally, Soft Actor-Critic achieved a 7%, 41.6%, and 42.8% lower inventory sales ratio than the S-policy, Twin Delayed Deep Deterministic Policy Gradient, and Trust Region Policy Optimization models, indicating its superior ability in making the inventory stocks available to make sales and profit from them.

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